In many industries, such as telecommunications, business-to-business transactional relationships occur on a massive scale and are recorded in (sometimes distributed) databases containing thousands of records, with large numbers of field per record. The long course of these business-to-business transactional relationships frequently results in the need to update these very large databases to reflect changes to product and service offerings. Changes can be triggered by promotions, customer reorganization and realignment, new product offering, and new contract structures, among other things. Changes can include both changes to the content of fields and changes to the field structure of records.
The need for manual updates to such databases, in order to promulgate such changes, creates a host of undesirable results. For users of these databases, updates have proven to be an highly labor-intensive activity, with individual changes requiring manual entry of changes to thousands or millions of records. In addition to the intense need for labor to manage such updates, the updates tie down system resources for long periods of time as the updates are entered by hand. Finally, manual entry of data for changes increases the likelihood of error in entry, corrupting the data that can, for many companies, represent one of the organization's most valuable assets. All of this resource consumption and error introduction generates adverse impact on the bottom line of the business enterprise attempting to use the database to conduct activities that generate revenue. The scale of the problem is growing exponentially with the growth of database systems for monitoring business relationships.